To solve the issue of video dehazing, there are two main tasks to attain: how to align adjacent frames to the reference frame; how to restore the reference frame. Some papers adopt explicit approaches (e.g., the Markov random field, optical flow, deformable convolution, 3D convolution) to align neighboring frames with the reference frame in feature space or image space, they then use various restoration methods to achieve the final dehazing results. In this paper, we propose a progressive alignment and restoration method for video dehazing. The alignment process aligns consecutive neighboring frames stage by stage without using the optical flow estimation. The restoration process is not only implemented under the alignment process but also uses a refinement network to improve the dehazing performance of the whole network. The proposed networks include four fusion networks and one refinement network. To decrease the parameters of networks, three fusion networks in the first fusion stage share the same parameters. Extensive experiments demonstrate that the proposed video dehazing method achieves outstanding performance against the-state-of-art methods.

翻译：为解决视频解层问题,需要完成两项主要任务:如何将相邻框架与参照框架相匹配;如何恢复参照框架。有些论文采取明确的方法(例如Markov随机场、光学流、变形变异、3D演化)将相邻框架与地貌空间或图像空间的参照框架相匹配,然后使用各种恢复方法实现最后解层结果。在本文件中,我们提出了视频解层的渐进调整和恢复方法。调整程序将相邻框架阶段相接相接的阶段相对齐,而不使用光学流估计。恢复进程不仅在调整过程中实施,而且还利用一个精细的网络来改善整个网络的脱层性能。拟议的网络包括四个聚变网络和一个改进网络。为降低网络参数,在最初的熔化阶段,三个电解层网络拥有相同的参数。广泛的实验表明,拟议的视频解层方法取得了与先进方法相比的杰出性能。